Joint microseismic moment-tensor inversion and location using P- and S-wave diffraction stacking
Bibliographic record
Abstract
ABSTRACT Microseismic location methods based on diffraction stacking, which does not require arrival picking, can yield accurate and reliable source locations for data with a low signal-to-noise ratio. However, due to the complex radiation pattern from a rupturing source, variation in the waveform polarities brings challenges to diffraction stacking-based methods. The current implementations of joint source mechanism inversion and location methods that only use compressional wave (P-wave) amplitudes have limitations in noise resistance and location accuracy. To mitigate those issues, we have developed a new method for joint microseismic moment-tensor inversion and event location using diffraction stacking with P- and S-waves amplitudes, both of which are used to invert for the moment tensor of a microseismic event; then, the inverted moment tensor is used to correct the waveform polarity changes before stacking. In addition, to expedite the large amount of calculations required for moment-tensor inversion at each potential source position and origin time, we develop an optimized grid search scheme and implement the algorithm with GPUs. Our location method does not require manual picking of the first arrivals, and it can automatically detect and locate microseismic events from continuous data. We first validated the method with two synthetic examples, and then we apply it to a surface monitoring data set for hydraulic fracturing at a shale gas well pad in the southern Sichuan Basin, China, where billions of cubic meters of shale gas are being produced annually. The locations of the microseismic events are nicely correlated with the fracturing stages, and the determined source mechanisms are also consistent with the expected fracture growth. Our method is feasible for microseismic surface monitoring with dense nodal arrays and can provide important information for fracture growth and regional stress characterization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".